On Extracting Regular Travel Behavior of Private Cars Based on Trajectory Data Analysis

Individuals driving private cars in the urban environments to fulll their travel needs have become one of the major daily activities. In particular, unlike the travel with floating cars such as taxis or ride-hailing vehicles, the travel behaviors of private cars exhibit a certain degree of regularity based on daily travel demands. Understanding such travel behavior facilitates many applications, e.g., intelligent transportation, smart city planning, and location-based services (LBS). In this paper, we focus on extracting the regular travel behavior of private cars based on trajectory data analysis. Specifically, first, we construct a trajectory similarity matrix since the similarity of trajectories reflects regular travel behavior. To achieve this, we introduce the stay time and propose an Improved Edit distance with Real Penalty (IERP) to measure the temporal-spatial distance between trajectories. Then, we employ Kernel Principal Component Analysis (KPCA) to reduce the feature dimension of the similarity matrix. Finally, to identify the travel regularity from large set of unlabeled trajectory data, we propose a classification method based on transfer learning to migrate existing knowledge with the purpose of solving learning problems in the target domain with only a small amount of labeled trajectory data or even no data. Extensive experiments using large-scale real-world trajectory data demonstrate that the proposed method can effectively identify the regular travel pattern of private cars and obtain superior accuracy when compared with the existing methods. Our findings on discovering regular travel behaviors of private cars can be directly applied to applications including destination prediction, PoI recommendation and route planning.